Long-Term Memory on SoloSoft

EverOS: Open-Source Long-Term Memory Operating System for Self-Evolving AI Agents

Fri, 01 May 2026 00:00:00 +0000

EverOS is an open-source long-term memory operating system for AI agents developed by EverMind, the AI research lab backed by Shanda Group. In an era where most AI agents operate with short-term, session-bound memory, EverOS introduces a persistent, self-organizing memory infrastructure that lets agents remember, reason, and evolve across sessions indefinitely.

The project has garnered over 4,200 GitHub stars and is backed by multiple peer-reviewed papers accepted at ACL 2026. Its monorepo architecture unifies four core components: EverCore (the self-organizing memory OS), HyperMem (the hypergraph memory engine), EverMemBench (a three-layer memory evaluation framework), and EvoAgentBench (an agent self-evolution benchmark).

graph TD
 A[Agent/LLM] --> B[EverCore]
 B --> C[HyperMem Hypergraph]
 B --> D[mRAG Multimodal Retriever]
 C --> E[Topic Hyperedges]
 C --> F[Event Hyperedges]
 C --> G[Fact Hyperedges]
 D --> H[Dense Vectors]
 D --> I[Sparse Keywords]
 D --> J[Multimodal Signals]
 B --> K[Evolved Skills]
 K --> A

What makes EverOS truly groundbreaking is its self-evolving capability. Agents can automatically distill skills and patterns from their task execution history, leading to a measured 234.8% relative improvement in complex task success rates over the baseline. This is not merely a caching layer – it is an active memory that grows smarter the more it is used.

HippoRAG: Neurobiologically Inspired Long-Term Memory for LLMs (NeurIPS 2024)

Fri, 01 May 2026 00:00:00 +0000

Retrieval-Augmented Generation (RAG) has become the standard approach for grounding LLM outputs in external knowledge. But standard RAG has a fundamental limitation: it treats each query independently, with no memory of past retrievals or ability to connect information across documents. HippoRAG takes inspiration from the human brain’s hippocampus to overcome this, creating a long-term memory system that dramatically improves multi-hop question answering.

Published at NeurIPS 2024 and available at github.com/OSU-NLP-Group/HippoRAG, HippoRAG combines LLMs with knowledge graphs in a framework modeled on the hippocampal indexing theory of human memory. The result is a RAG system that builds a persistent knowledge structure from documents, enabling it to answer complex questions that require connecting information across multiple sources – achieving approximately 20% improvement over standard RAG on multi-hop QA benchmarks.

MemPalace: The Best-Benchmarked Open-Source AI Memory System

Fri, 01 May 2026 00:00:00 +0000

AI agents struggle with long-term memory. Without it, every conversation starts from zero – no recollection of past tasks, user preferences, or ongoing projects. MemPalace takes direct aim at this limitation with a uniquely ambitious approach: a spatial hierarchy modeled on the ancient method of loci, the same mnemonic technique Roman orators used to memorize entire speeches. The result is an open-source AI memory system that achieves 96.6% recall on LongMemEval, the highest score among open-source systems at time of writing.

MemPalace is built by MemPalace, a team exploring biologically inspired architectures for AI memory. The project is local-first, meaning your agent’s memory lives on your machine rather than in a cloud API. This matters for both privacy and latency – memory retrieval happens in milliseconds without a network round trip.